A Multi-Agent Audit Framework for High-Stakes Reasoning: Evaluation and Interpretability in Clinical Mental Health Screening

• URL: http://arxiv.org/abs/2606.21123v1
• Date: Fri, 19 Jun 2026 05:51:03 GMT
• Title: A Multi-Agent Audit Framework for High-Stakes Reasoning: Evaluation and Interpretability in Clinical Mental Health Screening
• Abstract summary: We propose a Multi-Agent Audit Framework that simulates a collaborative, multi-step verification process.<n>We empirically validate this architecture in the sensitive domain of clinical mental health screening.<n>Our framework decomposes the reasoning process into a Perception Agent, Knowledge Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT) clinical inference, and a critical Audit verification stage.
• Score: 0.8758828205574085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-stakes reasoning tasks necessitate transparent and verifiable workflows, yet conventional single-model large language models (LLMs) often struggle with hallucination and low interpretability under zero-shot paradigms. To address this general AI challenge, we propose a Multi-Agent Audit Framework that simulates a collaborative, multi-step verification process. We empirically validate this architecture in the sensitive domain of clinical mental health screening using a modular LangChain workflow. Our framework decomposes the reasoning process into a Perception Agent, Knowledge Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT) clinical inference, and a critical Audit verification stage. We evaluated this framework on the DAIC-WOZ dataset using locally deployed open-source models. Experimental results demonstrate that our multi-agent pipeline significantly outperforms single-agent baselines, reducing the Mean Absolute Error (MAE) for PHQ-8 depression severity prediction from 5.35 to 5.02. By exposing cross-agent validation traces, the framework mitigates reasoning drift and provides highly interpretable diagnostic rationales, offering a generalizable paradigm for reliable AI-assisted decision support beyond isolated model scaling. We make data and code open access on GitHub for replicability.

Related papers

• Perception, Verdict, and Evolution: Hindsight-Driven Self-Refining Forensics Agent for AI-Generated Image Detection [80.15317858033534]
  ForeAgent is an agentic forensics framework for AI-generated image detection with iterative self-evolution.<n>ForeAgent achieves state-of-the-art performance on the Chameleon benchmark, reaching 82.18% accuracy.<n>ForeAgent produces more consistent and causally grounded reasoning compared to GPT-5 and GPT-5-mini.
  arXiv  Detail & Related papers  (2026-06-25T02:59:33Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• MAVEN: Multi-Agent Verification-Elaboration Network with In-Step Epistemic Auditing [18.35295672031847]
  MAVEN is a framework designed to transform LLMs into deliberate reasoners through explicit role-decoupling.<n>MAVEN consistently outperforms latent reasoning models such as GEMINI-3.1-Pro.<n>MAVEN is fully model-agnostic, serving as a strong and transferable reasoning booster.
  arXiv  Detail & Related papers  (2026-05-08T12:11:08Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• CARE: Towards Clinical Accountability in Multi-Modal Medical Reasoning with an Evidence-Grounded Agentic Framework [29.22693846221723]
  We introduce CARE, advancing Clinical Accountability in multi-modal medical Reasoning with an Evidence-grounded agentic framework.<n> CARE decomposes the task into coordinated sub-modules to reduce shortcut learning and hallucination.<n>Our CARE-Flow improves average accuracy by 10.9% over the same size (10B) state-of-the-art (SOTA)
  arXiv  Detail & Related papers  (2026-03-02T08:38:37Z)
• Strong Reasoning Isn't Enough: Evaluating Evidence Elicitation in Interactive Diagnosis [29.630872344186873]
  Interactive medical consultation requires an agent to proactively elicit missing clinical evidence under uncertainty.<n>Existing evaluations largely remain static or outcome-centric, neglecting the evidence-gathering process.<n>We propose an interactive evaluation framework that explicitly models the consultation process using a simulated patient and a revsimulated reporter grounded in atomic evidences.
  arXiv  Detail & Related papers  (2026-01-27T16:36:35Z)
• The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check [54.08619694620588]
  We present a comprehensive evaluation of dLLMs across two distinct agentic paradigms: Embodied Agents and Tool-Calling Agents.<n>Our results on Agentboard and BFCL reveal a "bitter lesson": current dLLMs fail to serve as reliable agentic backbones.
  arXiv  Detail & Related papers  (2026-01-19T11:45:39Z)
• An Agentic AI System for Multi-Framework Communication Coding [17.846847341760675]
  We developed a Multi-framework Structured Agentic AI system for Clinical Communication (MOSAIC)<n>MOSAIC is built on a LangGraph-based architecture that orchestrates four core agents, including a Plan Agent for codebook selection and workflow planning.<n>To evaluate performance, we compared MOSAIC outputs against gold-standard annotations created by trained human coders.
  arXiv  Detail & Related papers  (2025-12-09T14:46:16Z)
• CureAgent: A Training-Free Executor-Analyst Framework for Clinical Reasoning [23.353757415071104]
  Executor-Analyst Framework is a modular architecture that decouples the syntactic precision of tool execution from the semantic robustness of clinical reasoning.<n>Our approach underscores the potential of training-free architectural engineering, achieving state-of-the-art performance on CURE-Bench without the need for expensive end-to-end finetuning.
  arXiv  Detail & Related papers  (2025-12-05T09:56:58Z)
• Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making [80.94208848596215]
  We present a new concept called Catfish Agent, a role-specialized LLM designed to inject structured dissent and counter silent agreement.<n>Inspired by the catfish effect'' in organizational psychology, the Catfish Agent is designed to challenge emerging consensus to stimulate deeper reasoning.
  arXiv  Detail & Related papers  (2025-05-27T17:59:50Z)
• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.